An intelligent digital operation and maintenance management system and method for charging piles

Abstract

The present invention discloses an intelligent digital operation and maintenance management system and method for charging piles, which relates to the technical field of operation and maintenance of new energy infrastructure. It includes an information acquisition module that determines a delayed information flow channel from the charging pile operation and maintenance management system, obtains real-time discussion content, processes the discussion text using word segmentation technology to obtain semantic feature vectors, a problem recognition module that, if the confidence level of the semantic feature vector is higher than a preset threshold, determines it as a specific problem type through state space definition to obtain a classified problem label, and a historical matching module that obtains associated features with the historical problem library, matches historical problem records, designs a reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current problem. This intelligent digital operation and maintenance management system and method for charging piles continuously improves the accuracy of problem classification and solution matching, realizing the intelligence and high efficiency of charging pile operation and maintenance management.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy infrastructure operation and maintenance, and specifically relates to a smart digital operation and maintenance management system and method for charging piles. Background Art

[0002] The smart digital operation and maintenance management of the charging pile industry is a key area for promoting the efficient operation of new energy infrastructure. Its importance lies in ensuring the stability of the charging network and the continuous optimization of the user experience. With the popularization of electric vehicles, the demand for the intelligence of operation and maintenance management systems has become increasingly prominent. Especially in improving operation and maintenance efficiency and reducing fault response time, intelligent collaboration and decision support have become the core driving forces.

[0003] However, the current operation and maintenance management systems have significant limitations in collaboration and problem-solving. Most of the existing solutions rely on manual communication or simple work order systems, lacking real-time team collaboration mechanisms, resulting in lagging information transmission and low problem handling efficiency. In addition, the repeated exploration of known problems is widespread. The operation and maintenance team often wastes time due to the lack of an automated knowledge matching mechanism and cannot quickly reuse historical solutions. In this context, the technical implementation of real-time collaboration and problem identification has become the main challenge. First, the construction of a real-time collaboration platform needs to solve the instant communication needs across regions and multiple roles to ensure the efficient flow of information in complex operation and maintenance scenarios. Second, the intelligent identification of problem types faces technical difficulties. The system needs to accurately analyze the discussion content and associate it with historical problem patterns to avoid misjudgment or missed judgment. Finally, the realization of automatically matching relevant prototypes and generating actionable guidance requires the system to not only extract problem features but also present complex knowledge to operation and maintenance personnel in a simple and intuitive manner. These technical factors have not been solved, resulting in the operation and maintenance team often falling into an inefficient trial-and-error cycle when dealing with faults and being difficult to quickly locate and solve problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart digital operation and maintenance management system and method for charging piles, realizing efficient instant communication, and at the same time, by intelligently identifying the types of discussion problems, automatically matching relevant prototypes and generating actionable guidance, so as to avoid repeating the exploration of the solution paths of known problems.

[0005] To achieve the above object, the present invention provides the following technical solution: A smart digital operation and maintenance management system for charging piles, comprising:

[0006] An information acquisition module, which determines a delayed information transfer channel from the charging pile operation and maintenance management system, acquires real-time discussion content, and processes the discussion text using word segmentation technology to obtain semantic feature vectors;

[0007] A problem identification module. If the confidence level of the semantic feature vector is higher than a preset threshold, it is determined as a specific problem type through state space definition, and the classified problem label is obtained.

[0008] A historical matching module. It obtains the associated features with the historical problem library, matches the historical problem records, designs a reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current problem.

[0009] A step extraction module. It extracts the key operation steps, maps and associates the operation steps with the scenario labels, sorts the execution order of the emergency scenarios, and obtains the structured knowledge representation.

[0010] A guidance generation module. It generates actionable guidance, uses templated text generation technology to convert the operation steps into executable instructions for the operation and maintenance personnel, detects and identifies logical contradictions between the instructions, and determines the final guidance text.

[0011] An adaptation module. It integrates a complex scenario adaptation module, triggers an external data adjustment instruction content adapted to the fault codes and environmental parameters parsed according to the device status, and obtains the adapted guidance solution.

[0012] A feedback processing module. It obtains the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update.

[0013] A rule update module. It obtains the optimized classification weights and matching thresholds, uses a convergence evaluation model to evaluate the stability, and updates the dynamic rule library through the integration of feedback data to obtain the updated scenario label mapping rules.

[0014] Preferably, the information acquisition module determines the delayed information transfer channel from the charging pile operation and maintenance management system, obtains the real-time discussion content, and processes the discussion text using word segmentation technology. The semantic feature vector obtained includes:

[0015] Extract cross-regional communication data from the charging pile operation and maintenance logs, use distributed message queue technology to process high-concurrency requests, generate a structured instant messaging dataset. If the data volume exceeds the preset threshold, distribute the data to multiple queue nodes through a partitioning strategy to obtain a structured instant messaging dataset. For the structured instant messaging dataset, establish a real-time collaboration platform using the WebSocket protocol to achieve cross-regional multi-role two-way communication, determine a low-latency information transfer channel, monitor the channel status through the real-time collaboration platform. If it is detected that the latency exceeds the preset threshold, adjust the load balancing of the communication nodes to obtain a stable low-latency channel. Obtain the real-time discussion text from the low-latency information transfer channel, apply word segmentation technology to process the text to generate a word segmentation sequence of the text, use a pre-trained word embedding model to convert the word segmentation sequence into a word vector sequence to obtain a preliminary semantic representation of the text. Obtain temperature and humidity environmental parameter data through a sensor interface, perform normalization on the environmental parameters using standardization processing to generate an environmental feature vector. If the dimension of the environmental feature vector does not match the dimension of the semantic representation, adjust the dimension through linear transformation to obtain an aligned environmental feature vector. For the preliminary semantic representation of the text and the aligned environmental feature vector, use an attention mechanism to fuse the information of both to generate a comprehensive semantic feature vector, process the comprehensive semantic feature vector through a multi-layer perceptron to obtain the final semantic feature vector of the problem description, extract key semantic units from the final semantic feature vector, use a clustering algorithm to group the semantic units to generate a semantic classification result of the problem description, and generate an optimized scheduling instruction for cross-regional collaboration based on the semantic classification result to determine the priority ranking of the operation and maintenance tasks.

[0016] Preferably, for the problem recognition module, if the confidence of the semantic feature vector is higher than the preset threshold, it is judged as a specific problem type through state space definition, and the classified problem labels obtained include:

[0017] Extract the semantic feature vector from the problem description, perform feature encoding using a pre-trained deep learning network to obtain an initial semantic representation, perform classification processing on the initial semantic representation through the pre-trained deep learning network to obtain a classification confidence distribution. If the classification confidence distribution is higher than the preset threshold, use a state space model to verify the problem type to determine a preliminary problem type label. According to the preliminary problem type label, obtain an association rule from a preset knowledge base to get a label correction basis, adjust the preliminary problem type label through the label correction basis to generate a corrected problem type label, use the corrected problem type label, combined with the semantic feature vector, to construct a label semantic consistency check to judge the final problem type label, extract the classification result from the final problem type label to generate a problem type classification output.

[0018] Preferably, the historical matching module obtains the associated features with the historical question library, matches the historical question records, designs an evaluation function through a reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current question, including:

[0019] Obtain the classification label from the input question tags, generate the associated features through the feature extraction method to obtain the feature vector representation, use the vector similarity calculation method to compare the feature vector with the feature vectors recorded in the historical question library to obtain the similarity score. If the similarity score is greater than the preset threshold, obtain the corresponding matching record from the historical question library to determine the candidate historical solution. Adopt a reward function, combine the classification label and the context information of the matching record to calculate the evaluation score of each candidate solution to obtain the accuracy ranking. According to the accuracy ranking, obtain the record with the highest score from the candidate solutions to determine the most similar historical solution. By analyzing the differences between the feature vector of the most similar solution and the current question tags, adjust the feature weights to obtain the optimized solution representation. Extract the key parameters from the optimized solution representation to generate the final solution output.

[0020] Preferably, the step extraction module extracts the key operation steps, maps and associates the operation steps with the scenario tags, and sorts the execution order of the emergency scenarios to obtain the structured knowledge representation, including:

[0021] Obtain the records from the historical solution database, use the text parsing technology to extract the key operation steps to obtain the operation step set. If the operation step set contains duplicate items, generate a unique operation step list through deduplication processing to obtain the refined step set. Use the knowledge graph technology to perform semantic matching on the refined step set and the preset scenario tags to generate the mapping relationship between the steps and the tags. According to the mapping relationship, obtain the scenario tag weights corresponding to each operation step, judge whether the weights exceed the preset threshold to obtain the high-correlation tag set. For the high-correlation tag set, adopt the priority sorting algorithm to sort the emergency scenarios according to the instruction priority to generate the scenario priority sequence. Through the structured encoding technology, associate the scenario priority sequence with the operation step set to generate the structured knowledge representation. If there are missing fields in the structured knowledge representation, fill in the missing parts through the semantic completion technology to obtain the complete knowledge representation.

[0022] Preferably, the guidance generation module generates the actionable guidance, uses the templated text generation technology to convert the operation steps into executable instructions for the operation and maintenance personnel, and detects and identifies the logical contradictions between the instructions to determine the final guidance text, including:

[0023] Obtain structured knowledge, extract operation and maintenance related rules and data from a preset knowledge base to obtain a knowledge representation. Adopt templating technology to generate initial operation and maintenance instructions for the rules and data in the knowledge representation. Determine an executable instruction set. Through conflict detection, analyze the logical relationships in the executable instruction set. If there are contradictions, mark the relevant instructions to obtain a contradiction identifier. According to the contradiction identifier, perform rule analysis, extract conflict points from the marked instructions, and determine a logical contradiction set. For the logical contradiction set, adopt text transformation technology to adjust the expression or order of the contradictory instructions to obtain an optimized instruction set. Through templating technology, integrate the optimized instruction set to generate a final text, and determine the operation and maintenance guidance text. If there are still contradictions in the optimized instruction set, repeat conflict detection and rule analysis to obtain a final contradiction-free guidance text.

[0024] Preferably, the adaptation module integrates a complex scenario adaptation module, triggers an external data adjustment instruction content adapted to the fault code parsed according to the device state and environmental parameters, and the obtained adapted guidance solution includes:

[0025] Obtain device state data in a complex scenario, analyze the signal characteristics of the trigger fault code to determine the initial fault state. Through the analysis process, extract key variables from the environmental parameters to obtain environmental impact factors associated with the fault code. If the environmental impact factor exceeds a preset threshold, adjust the instruction content according to external data to generate a temporary optimized instruction. For the temporary optimized instruction, adopt an adaptation adjustment mechanism to integrate the fault code and environmental parameters to obtain a fine-tuning instruction set. Record the revision log of the fine-tuning instruction set, store the adjustment details, generate a dynamic version of the optimized solution. According to the optimized solution of the dynamic version, obtain the real-time feedback of the device state, judge the instruction execution effect. If the execution effect does not meet the preset standard, trigger the mechanism to re-parse the fault code to obtain an updated optimized solution.

[0026] Preferably, the feedback processing module obtains the feedback data after execution, stores the feedback and interaction records, and adjusts the optimization action selection and model parameter update, including:

[0027] Obtain feedback data from the interaction record, judge the data integrity through a preset threshold, obtain the filtered feedback data set, adopt an experience replay mechanism to store the filtered feedback data set and action records, generate a structured replay buffer, analyze the action records in the replay buffer through an exploration strategy, adjust the action selection probability, obtain an optimized action distribution, update the model parameters according to the optimized action distribution, adopt a gradient descent algorithm for iterative training, generate an updated classification network. If the confidence of the problem type output by the classification network is lower than the preset threshold, obtain similar problem records from the historical solutions, judge the matching parameters, combine the matching parameters with the output of the classification network, adjust the weights of the historical solutions, obtain an accurate problem type classification result, generate corresponding solution parameters according to the accurate problem type classification result, and store them in the replay buffer to optimize subsequent interactions.

[0028] Preferably, the feedback processing module, which obtains the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update, includes:

[0029] Analyze the input data through a problem classification network to obtain classification weights and matching thresholds. If the classification weights are lower than the preset threshold, adjust the network parameters to obtain optimized classification weights. Extract features from the historical solutions, combine the optimized classification weights, and determine the matching thresholds. If the deviation between the matching thresholds and the features of the historical solutions exceeds the range, adjust the thresholds through weighted averaging to obtain stable matching thresholds. Adopt a convergence evaluation method to process the optimized classification weights and stable matching thresholds, judge the model stability. If the convergence evaluation result shows fluctuations, adjust the parameters through iterative optimization to obtain stable model parameters. Obtain the feedback data, analyze its relevance to the dynamic rule base, update the content of the rule base, fuse the feedback data through a data integration method to generate an updated dynamic rule base. According to the updated dynamic rule base, extract scene label features to generate preliminary mapping rules. If the matching degree between the preliminary mapping rules and the scene labels is insufficient, optimize through rule adjustment to obtain optimized mapping rules. Process the scene labels through the optimized mapping rules to generate the final scene label mapping result. Adopt a consistency check to verify the mapping result to obtain the final scene label mapping rules. Extract key parameters from the final scene label mapping rules to update the initial configuration of the problem classification network, and adjust the network structure through parameter backpropagation to obtain an optimized classification network model.

[0030] A method for intelligent digital operation and maintenance management of charging piles, using the described intelligent digital operation and maintenance management system for charging piles, the method includes:

[0031] From the charging pile operation and maintenance management system, determine the delay information transfer channel, obtain the real-time discussion content, and process the discussion text using word segmentation technology to obtain semantic feature vectors;

[0032] If the confidence of the semantic feature vector is higher than a preset threshold, it is determined as a specific problem type through state space definition, and the classified problem label is obtained;

[0033] Obtain the associated features with the historical problem library, match the historical problem records, evaluate the matching accuracy through the design of the reward function, and determine the historical solution most similar to the current problem;

[0034] Extract the key operation steps, map and associate the operation steps with the scenario labels, sort the execution order of the emergency scenarios, and obtain the structured knowledge representation;

[0035] Generate actionable guidance, use the templated text generation technology to convert the operation steps into executable instructions for the operation and maintenance personnel, detect and identify the logical contradictions between the instructions, and determine the final guidance text;

[0036] Integrate the complex scenario adaptation module, trigger the external data adjustment instruction content adapted to the fault code parsed according to the device state and the environmental parameters, and obtain the adapted guidance solution;

[0037] Obtain the feedback data after execution, store the feedback and interaction records, and adjust and optimize the action selection and model parameter update;

[0038] Obtain the optimized classification weights and matching thresholds, use the convergence evaluation to evaluate the model stability, and update the dynamic rule library through the integration of the feedback data to obtain the updated scenario label mapping rules.

[0039] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0040] The intelligent digital operation and maintenance management system and method for charging piles process high-concurrency requests through a distributed message queue, use WebSocket to achieve cross-regional multi-role two-way communication, and build a low-latency information flow channel. For real-time discussion content, the present invention uses the word segmentation technology and environmental parameter fusion to generate the semantic feature vector of the problem description, and classifies the problem through a deep learning network. Subsequently, the present invention matches the historical solutions, extracts the key operation steps, generates the structured knowledge representation, and converts it into executable instructions. The present invention also integrates a complex scenario adaptation module to dynamically adjust the instruction content according to the device state and environmental parameters. Through the experience replay mechanism and exploration strategy optimization, the present invention continuously improves the accuracy of problem classification and solution matching, realizing the intelligence and high efficiency of charging pile operation and maintenance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is the system module connection diagram of the present invention;

[0042] Figure 2 It is the method flow diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0044] As Figure 1 shown, the present invention provides a technical solution: an intelligent digital operation and maintenance management system for charging piles, including an information acquisition module, which determines a delayed information flow channel from the charging pile operation and maintenance management system, acquires real-time discussion content, processes the discussion text using word segmentation technology, and obtains a semantic feature vector; a problem recognition module, if the confidence of the semantic feature vector is higher than a preset threshold, determines it as a specific problem type through state space definition, and obtains a classified problem label; a historical matching module, acquires associated features with the historical problem library, matches historical problem records, evaluates the matching accuracy through the design of a reward function, and determines the historical solution most similar to the current problem; a step extraction module, extracts key operation steps, maps and associates the operation steps with scene labels, sorts the execution order of emergency scenes, and obtains a structured knowledge representation; a guidance generation module, generates actionable guidance, converts the operation steps into executable instructions for operation and maintenance personnel using template-based text generation technology, detects and identifies logical contradictions between instructions, and determines the final guidance text; an adaptation module, integrates a complex scene adaptation module, triggers an external data adjustment instruction content adapted to the fault code parsed according to the device state and the environmental parameters, and obtains an adapted guidance solution; a feedback processing module, acquires the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update; a rule update module, acquires the optimized classification weights and matching thresholds, evaluates the model stability using a convergence evaluation model, and updates the dynamic rule library through feedback data integration, and obtains an updated scene label mapping rule.

[0045] The system first accesses the real-time data channel of the charging pile operation and maintenance management system through the information acquisition module. The semantic features of the discussion information of the operation and maintenance group are extracted with the help of the word segmentation algorithm in natural language processing to generate a problem feature vector that can be used for judgment. Then, the problem identification module classifies the problem according to the confidence of the feature vector and the state space mapping rule to obtain a clear problem label. The system calls the historical matching module to extract the features of the corresponding labels from the historical problem library and compare them with the current problem. The matching accuracy is evaluated using the reward function, and the closest historical solution is finally selected. Subsequently, the step extraction module extracts the key operation process from the solution, associates it with the pre-marked scene labels, and automatically sorts it according to the urgency of the scene, thereby forming a structured task execution sequence. The structured step generates the operation and maintenance instructions in natural language form through the guidance generation module combined with the template technology. The system synchronously performs logical consistency detection, eliminates contradictory steps, and forms a highly consistent final guidance text. On this basis, the adaptation module automatically calibrates the operation instructions according to the current equipment fault code and environmental parameters to achieve scene adaptation. Finally, the post-execution data is stored and analyzed through the feedback processing module, and handed over to the rule update module for dynamic adjustment of classification weights and thresholds, so that the system has the ability of continuous learning and optimization.

[0046] The present invention can significantly improve the intelligence level of the charging pile operation and maintenance process, realize closed-loop management from fault identification, solution matching, operation guidance, environmental adaptation to feedback optimization, and solve key problems in existing systems such as low problem identification efficiency, vague guidance operation, and lack of dynamic learning. In terms of performance, it improves the efficiency and accuracy of problem handling, reduces labor costs and equipment failure rates; in terms of scalability, the system supports operation and maintenance adaptation in multiple scenarios and multi-parameter environments, improving the stability and robustness of the system; at the same time, the rule update mechanism ensures the continuous evolution of the system's decision-making ability, and has good long-term operation and maintenance value.

[0047] For example, taking a certain brand of electric vehicle charging station in City A as an example, the station frequently has current overload alarms during the high temperature period in summer. The operation and maintenance personnel submitted a discussion on the management platform about "abnormal tripping of pile No. 13 at the station due to high temperature". The system information acquisition module captured keywords such as "high temperature" and "tripping" and judged it as a "temperature control abnormality" type of problem through semantic recognition. Then, it matched similar events and corresponding solutions from the same period last year from the historical problem library, including measures such as "reducing charging current" and "enhancing ventilation". The system automatically extracts the operation steps "adjusting the module output current to less than 20A" and "starting fan mode 2", etc., and converts them into clear operation instructions. After scene adaptation, because the ambient temperature on that day was 38°C, the system automatically recommends "extending the cooling time by 3 minutes" and outputs a complete guidance text. After the operation and maintenance personnel follow the instructions, the system records the feedback and promotes the update of the rule library to optimize the response strategy for similar scenarios in the future.

[0048] An information acquisition module determines a delay information transfer channel from a charging pile operation and maintenance management system, obtains real-time discussion content, processes the discussion text using word segmentation technology to obtain semantic feature vectors, including extracting cross-regional communication data from charging pile operation and maintenance logs, processing high-concurrency requests using distributed message queue technology to generate a structured instant messaging dataset. If the data volume exceeds a preset threshold, the data is distributed to multiple queue nodes through a partitioning strategy to obtain a structured instant messaging dataset. For the structured instant messaging dataset, a real-time collaboration platform is established using the WebSocket protocol to achieve cross-regional multi-role two-way communication, determine a low-latency information transfer channel, monitor the channel status through the real-time collaboration platform. If it is detected that the delay exceeds the preset threshold, the load balancing of communication nodes is adjusted to obtain a stable low-latency channel. Real-time discussion text is obtained from the low-latency information transfer channel, the text is processed using word segmentation technology to generate a word segmentation sequence of the text, and a pre-trained word embedding model is used to convert the word segmentation sequence into a word vector sequence to obtain a preliminary semantic representation of the text. Environmental parameter data such as temperature and humidity is obtained through a sensor interface, and the environmental parameters are normalized using standardization processing to generate an environmental feature vector. If the dimension of the environmental feature vector does not match the dimension of the semantic representation, the dimension is adjusted through linear transformation to obtain an aligned environmental feature vector. For the preliminary semantic representation of the text and the aligned environmental feature vector, the attention mechanism is used to fuse the information of both to generate a comprehensive semantic feature vector. The comprehensive semantic feature vector is processed by a multi-layer perceptron to obtain the final semantic feature vector of the problem description. Key semantic units are extracted from the final semantic feature vector, and a clustering algorithm is used to group the semantic units to generate a semantic classification result of the problem description. According to the semantic classification result, an optimized scheduling instruction for cross-regional collaboration is generated, and the priority ranking of operation and maintenance tasks is determined.

[0049] The workflow of the present invention first extracts communication data between multiple regions from the operation and maintenance system logs of charging piles. These data sources include device alarm records, maintenance feedback, monitoring information upload, etc. When processing this communication data, in order to handle high-concurrency scenarios, the system introduces distributed message queue technology. When new data arrives, the system first reads the keywords contained in this piece of communication data, such as "high temperature", "tripping", etc. Then, the system processes these keywords through a hash function to obtain an integer value. This integer value will be used to perform a modulo operation with the total number of channels in the current system to determine which queue node this piece of data should be sent to for processing. For example, if the system has a total of 10 message processing nodes deployed, keywords with a hash result of 27 will be sent to the processing node numbered 7. This process ensures that all data is reasonably distributed, preventing a certain node from being delayed due to an excessive workload. The system will monitor the processing time of all communication records. It records the difference between the sending time and the receiving time of each message to calculate the delay time of the communication channel. If it is detected that the delay time of a certain message exceeds the system-set threshold, such as 100 milliseconds, the system will calculate the request load of each current node. This load refers to the total number of messages currently being processed by a node divided by the processing capacity of the node per unit time (such as the number of messages that can be processed per second). The system will reassign tasks preferentially to the node with the smallest load value to ensure that the overall communication delay is controlled within an acceptable range. After the communication channel is stable, the system obtains the real-time discussion text content from it. These texts will first be processed by a Chinese word segmentation tool to obtain a word segmentation sequence. For example, a text of "site failure, unable to charge" may be segmented into words such as "site", "failure", "unable", "charge", etc. Each word will be converted into a high-dimensional vector through a pre-trained word embedding model (such as Word2Vec or BERT's Token Embedding). Assuming the output dimension of the model is 300 dimensions, then each word will be converted into a vector containing 300 floating-point numbers. At the same time, the system obtains the environmental parameters of the current site through connected environmental sensors, such as the temperature is 34°C and the humidity is 82%. The system inputs these values into a normalization function for normalization processing. Assuming the average temperature is 25°C and the standard deviation is 5°C, then the normalized temperature is (34 - 25) / 5 = 1.8. Similarly, the humidity is normalized in the same way. The normalized environmental vector will finally be expressed in a fixed format, such as [1.8, 1.4]. If the dimension of this environmental vector is not 300 (inconsistent with the dimension of the word vector), the system will perform dimension adjustment on it through a linear transformation module. The specific method is to multiply the original two-dimensional vector by a weight matrix with 2 rows and 300 columns and add a bias term to finally obtain a 300-dimensional vector to achieve alignment with the word vector in format. The system then inputs the word vector sequence and the environmental vector into an attention fusion module.In this module, the system calculates the degree of association between each word vector and the environmental vector. The result will be used as a weight to participate in the weighted summation process of word vectors, and finally generate a comprehensive vector representing the common semantics of the full text content and environmental factors. This comprehensive vector is fed into a multi-layer neural network. This network contains at least two layers, and each layer consists of a set of linear transformations, non-linear activation functions (such as ReLU), and possible regularization operations. The system inputs the vector layer by layer into the network and outputs a final semantic representation result. The system then extracts the semantic core part from this semantic vector, such as keywords "high temperature", "tripping", etc., and forms a set of semantic units. The system then calls a clustering algorithm (such as K-means) to automatically group according to the semantic features of these units, and classify problems with similar meanings into the same type. According to the clustering results, the system prioritizes the tasks. For example, if a category of problems belongs to the high-risk equipment failure category, the system assigns a higher priority to it and pushes the processing task to the scheduling module. At the same time, combined with the availability of operation and maintenance personnel in each place, an optimal cross-regional collaborative task allocation plan is generated, so as to achieve efficient and intelligent joint response between multiple places.

[0050] For example, in an electric bus system operating in a coastal city, due to the sudden increase in humidity in spring, the communication modules of some charging piles are frequently disconnected. The operation and maintenance personnel reported in the platform that "multiple charging piles at the South Fourth Road bus stop in the north area are disconnected". The system captured this text content in real time and extracted semantic features. After combining with the humidity data provided by the sensor (higher than 90%), the semantic weight of the environmental interference factor was strengthened through the attention mechanism. Finally, the model identified it as a problem of "environment-induced communication interruption", automatically dispatched network engineers and environmental detection personnel to the scene, and ranked the task priority as the highest. The instruction clearly required checking the tightness of the communication module and updating the moisture-proof cover sealing strip, greatly improving the response efficiency and maintenance accuracy.

[0051] For the problem identification module, if the confidence of the semantic feature vector is higher than the preset threshold, it is judged as a specific problem type through the state space definition. The obtained classified problem labels include extracting the semantic feature vector from the problem description, using a pre-trained deep learning network for feature encoding to obtain the initial semantic representation, classifying the initial semantic representation through the pre-trained deep learning network to obtain the classification confidence distribution. If the classification confidence distribution is higher than the preset threshold, then use the state space model to verify the problem type, determine the preliminary problem type label, obtain the association rule from the preset knowledge base according to the preliminary problem type label, get the label correction basis, adjust the preliminary problem type label through the label correction basis, generate the corrected problem type label, use the corrected problem type label, combine with the semantic feature vector, construct the label semantic consistency check, judge the final problem type label, and extract the classification result from the final problem type label to generate the problem type classification output.

[0052] The system extracts semantic feature vectors from the discussion text through a semantic encoder and inputs them into a pre-trained deep neural network for initial semantic encoding. Let the input be vector X, and this vector will be encoded through the following operations: The weight matrix of the first layer is multiplied by the input vector and added with a bias term, and after being processed by the activation function, a hidden representation is obtained, that is: the output is equal to the weight of the first layer × input vector + bias term). If the deep neural network adopts a three-layer structure, the final output is the initial semantic representation for subsequent classification. Subsequently, this representation is fed into a classifier module (such as a Softmax classification layer) to calculate the probability distribution of each problem category, in the form of: the probability of each category = the output value processed by the exponential function / the sum of the exponential values of the outputs of all categories. For example, if the system has three types of problems (communication anomaly, current anomaly, control logic failure), and its output is [0.2, 0.7, 0.1], then the confidence level of the communication anomaly is 0.2. The system sets a confidence threshold (such as 0.6). If the confidence level of a certain category exceeds this value, the classification result is determined to be credible. Then it enters the state space model verification. The state space S is composed of the set of problem states recognized by the current system, and the transition probability between each state is defined as: state transition probability = the number of times state A transfers to state B in historical samples / the total number of times state A appears. If the transition probability of the current classification label to the previous state is higher than a preset threshold (such as 0.4), then it is confirmed as a preliminary problem label. Subsequently, the system searches the knowledge base for correction rules related to this problem type according to the preliminary problem label. For example: if the "communication anomaly" problem usually co-occurs with "network module offline" or "IP conflict", the system will determine whether these semantic features or environmental evidences exist. If so, the confidence level of this label is strengthened. The label correction operation is specifically determined by the linear weighted calculation of the rule matching score and the current label confidence level: corrected confidence level = original confidence level × (1 - correction coefficient) + matching strength × correction coefficient. Among them, the correction coefficient is set empirically (such as 0.3), and the matching strength is quantified according to the rule hit degree (such as 0.8), and finally the corrected label is generated. Finally, the system performs a consistency check on the corrected label and the original semantic feature vector, and determines its logical consistency by calculating the semantic similarity (such as cosine similarity). If the similarity is higher than the set threshold (such as 0.7), then this label is confirmed as the final problem type label and the classification result is output.

[0053] For example, at a charging pile site in a certain area, the system receives a discussion text "The device network is disconnected and it is often impossible to remotely restart". The system inputs its semantic features into the model and initially classifies it as "communication anomaly". The confidence level of this classification reaches 0.72, exceeding the threshold of 0.6, so it is considered valid. The state space model detects that the previous problem was "server maintenance", and the historical probability of transitioning from "server maintenance" to "communication anomaly" is 0.45, which is higher than the transition threshold. The knowledge base finds that "network disconnection" often co-occurs with "IP conflict". The text mentions "impossible to remotely restart", and this symptom matches the co-occurrence rule. The system gives an additional confidence correction and finally determines it as "communication module disconnection" through semantic consistency verification, and outputs this classification result for subsequent module processing.

[0054] The historical matching module obtains the associated features related to the historical problem library, matches the historical problem records, designs an evaluation function through the reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current problem. This includes obtaining the classification label from the input problem label, generating the associated features through the feature extraction method to obtain the feature vector representation, using the vector similarity calculation method to compare the feature vector with the feature vectors of the records in the historical problem library to obtain the similarity score. If the similarity score is greater than the preset threshold, the corresponding matching record is obtained from the historical problem library to determine the candidate historical solution. The reward function is used to calculate the evaluation score of each candidate solution by combining the classification label and the context information of the matching record to obtain the accuracy ranking. According to the accuracy ranking, the record with the highest score is obtained from the candidate solutions to determine the most similar historical solution. By analyzing the differences between the feature vector of the most similar solution and the current problem label, the feature weights are adjusted to obtain the optimized solution representation, and the key parameters are extracted from the optimized solution representation to generate the final solution output.

[0055] This system calls the feature extraction algorithm by obtaining the classification tags of the current problem and using the problem type indicated by the tags. This algorithm can include methods such as keyword extraction, context encoding, TF-IDF vectorization, sentence vector embedding, etc., to convert the problem semantics into a vector form with a fixed dimension, such as a semantic feature vector containing 300 numerical values. The system then compares the feature vector of the current problem with the feature vector of each record in the historical problem library, and uses cosine similarity to calculate the similarity between the two. The specific calculation method is as follows: multiply the two vectors to get the numerator part, then calculate the product of the norms of the two vectors respectively as the denominator, and finally take the ratio of the two. The closer the ratio result is to 1, the higher the similarity. For example, if the current vector is vector A and the historical record vector is vector B, its similarity is the cosine value of the angle between A and B. The system sets a similarity threshold, such as 0.75. If the similarity of a certain historical record is higher than this value, it is considered a candidate record. All candidate records are included in the next step of processing, that is, the reward function evaluation. The reward function comprehensively considers whether the classification tags of the current problem match the semantic context of the historical record. The form of the reward function is: matching score = similarity score × semantic overlap degree × label consistency weight. Among them: the similarity score is the vector cosine similarity calculated in the previous step; the semantic overlap degree is obtained by the ratio of the number of overlapping keywords in the text to the total number of keywords; the label consistency weight is an empirically set value, such as 1 for complete consistency and 0.7 for partial consistency. The system sorts all candidate solutions in descending order according to the matching score, and takes the record with the highest score as the most similar historical solution. After that, the system further analyzes the feature differences between the feature vector of this optimal record and the current problem. The system applies amplification or attenuation factors to the dimensions with large weights in the difference vector to adjust the weight distribution of the feature vector of the current problem, so as to form an optimized feature representation closer to the historical successful solution. Finally, the system extracts the parameters strongly related to the actual execution operations from the optimized feature vector, such as "fault type", "processing order", "operation instruction number", etc., and outputs a set of specific execution plans for the operation and maintenance personnel.

[0056] For example, at a bus charging station in the northern part of a certain city, the system receives the problem label "severe voltage fluctuation". After vector modeling, the similarity between its feature vector and the record of "interruption caused by frequent fluctuation of charging current" in the historical database reaches 0.82, exceeding the system-set threshold of 0.75. The candidate records include 5 solutions such as "poor ground wire contact" and "current induction interference". After calculating through the reward function, the historical record number HX107 has the highest matching score because the semantic content mentions "fluctuation" and "instantaneous current increase" many times. The system further compares the parts of the processing sequence parameters extracted from HX107 that do not match the current environmental parameters, and before outputting the final solution, adjusts the "power-off protection delay" value in the parameters to 3 seconds recommended by the current environment. The finally generated execution plan includes specific operation instructions such as checking the looseness of the grounding cable, increasing the power-off delay, and resetting the power management module.

[0057] The step extraction module extracts key operation steps, maps and associates the operation steps with scenario labels, and sorts the execution order of emergency scenarios to obtain a structured knowledge representation, including obtaining records from the historical solution database, using text parsing technology to extract key operation steps to obtain an operation step set. If the operation step set contains duplicate items, a unique operation step list is generated through deduplication to obtain a refined step set. Using knowledge graph technology, semantic matching is performed on the refined step set and preset scenario labels to generate a mapping relationship between steps and labels. According to the mapping relationship, the scenario label weights corresponding to each operation step are obtained, and it is judged whether the weights exceed the preset threshold to obtain a set of highly relevant labels. For the set of highly relevant labels, a priority sorting algorithm is used to sort the emergency scenarios according to the instruction priority to generate a scenario priority sequence. Through structured encoding technology, the scenario priority sequence is associated with the operation step set to generate a structured knowledge representation. If there are missing fields in the structured knowledge representation, the missing parts are filled through semantic completion technology to obtain a complete knowledge representation.

[0058] This module first retrieves records from the historical solution database that match the current problem tags. The system uses natural language processing techniques (such as dependency syntactic analysis and action recognition) to parse the text, identify verb phrases with operation intentions and parameter conditions, such as "disconnect the power supply", "restart the module", etc., and form a set of operation steps. If there are steps in the set that are semantically repetitive or have slightly different expressions, such as "turn off the power supply" and "power-off operation", the system uses a text similarity calculation method (such as cosine similarity of sentence vectors) to determine whether they are semantically repetitive. If the similarity is greater than the threshold (such as 0.85), only one item is retained to obtain a refined set of steps. The system then calls the knowledge graph engine to embed each operation step into the semantic vector space and match it with the preset set of scenario tags. The matching method follows the following rules: calculate the semantic relevance between each operation step and the scenario tag, and obtain a score through vector cosine similarity; according to the matching score and the weight model, obtain the matching weight of the operation step with each scenario tag; for example, the matching degree of an operation step with the "overload power-off" tag is 0.9, and with the "insufficient power" tag is 0.3. If the system sets the matching threshold to 0.7, only the scenario tags with a matching degree greater than 0.7 are regarded as highly relevant tags. For the selected set of highly relevant tags, the system uses a priority sorting algorithm to sort them. This algorithm comprehensively considers the following indicators: the danger level of the scenario corresponding to the tag; the frequency of the step appearing in the historical solutions; the operation time and execution cost. Finally, a scenario priority sequence is obtained. For example: "power-off protection" > "short-circuit detection" > "communication reset". Then, the system binds the above scenario sequence and the corresponding operation steps one by one through a structured encoding method to form a structured knowledge representation in the form of triples: [operation step, scenario tag, priority weight]. If an operation step lacks execution parameters or the tags are incomplete, the system calls the semantic completion model to infer the missing fields based on the existing semantic context to ensure the integrity and executability of the structured representation. For example, taking the operation and maintenance record of a charging station "High-temperature protection trigger detected, it is recommended to disconnect the main power supply and check the module cooling system" as an example, the system identifies "disconnect the main power supply" and "check the module cooling system" as key operation steps. After deduplication, the unique expression is retained. The matching degree of the step "disconnect the main power supply" with the "temperature control protection scenario" in the knowledge graph is 0.92, and the matching degree of "check the module cooling system" with the "equipment maintenance scenario" is 0.88, both higher than the threshold of 0.7. The sorting logic determines that the former is more urgent with a priority of 1, and the latter is 2. Finally, the system outputs the structured triples: [disconnect the main power supply, temperature control protection, 1], [check the module cooling system, equipment maintenance, 2]. At the same time, the "module ID" field is completed, and a complete operation task package is output for the front-end platform to call.

[0059] The guidance generation module generates actionable guidance. It uses templated text generation technology to convert operation steps into executable instructions for operation and maintenance personnel, detects and identifies logical contradictions between instructions, and determines that the final guidance text includes obtaining structured knowledge, extracting operation and maintenance-related rules and data from a preset knowledge base to obtain a knowledge representation. Using templated technology, initial operation and maintenance instructions are generated for the rules and data in the knowledge representation. A set of executable instructions is determined. Through conflict detection, the logical relationships in the set of executable instructions are analyzed. If there are contradictions, the relevant instructions are marked to obtain a contradiction identifier. According to the contradiction identifier, rule analysis is performed, the conflict points are extracted from the marked instructions, and a logical contradiction set is determined. For the logical contradiction set, text conversion technology is used to adjust the expression or order of the contradictory instructions to obtain an optimized instruction set. Through templated technology, the optimized instruction set is integrated to generate the final text, and the operation and maintenance guidance text is determined. If there are still contradictions in the optimized instruction set, conflict detection and rule analysis are repeated to obtain the final contradiction-free guidance text.

[0060] The system first obtains the sorted operation steps and their corresponding execution parameters from the structured knowledge representation, such as operation actions, device numbers, execution time windows, etc. These contents are input into the knowledge template engine, and the system will call the rule entries stored in the knowledge base, such as operation and maintenance specifications like "Module disassembly is prohibited before power-off" and "Hot plugging is not allowed", and combine them with the operation steps to form specific knowledge expressions. Subsequently, the system uses the templatized generation method to convert the knowledge expressions into initial operation and maintenance instructions in natural language form. For example, the template item is: "Please perform the [action] operation on the [device], time requirement: [time]." The initial instruction list is generated in batches through variable substitution technology to form an "executable instruction set". The system then calls the conflict detection module, which uses Boolean logic rules to analyze whether there are logical inconsistencies between the instructions. For example, if one instruction requires "Turn off the main power" and another requires "Detect the voltage of the power module", there is a contradiction in the execution conditions between the two. The detection algorithm is based on the following logic: If the result variable in instruction A conflicts with the precondition of instruction B and the execution times overlap, then record this contradiction; the logical conflict score for each pair of instructions is calculated by the following method: Logical contradiction score = Condition conflict intensity × Time overlap coefficient. Among them: The condition conflict intensity is scored according to the degree of semantic opposition, set to 1 for complete opposition and 0.5 for weak conflict; the time overlap coefficient is the ratio of the intersection length of the execution time windows of the two instructions to the total window length. If the score is greater than the set threshold (such as 0.6), then this pair of instructions is marked as contradictory. The system forms a "logical contradiction set" for all contradictory instructions and calls the rule analysis module to deeply analyze the context logic and sequential relationship of these instructions. Subsequently, text conversion technology is used to modify the contradictory instructions, including: semantic layer adjustment, such as changing "Power off immediately" to "Power off after detection is completed"; sequential adjustment, such as delaying the "Power off" operation until after the "Detect module"; instruction reconstruction, such as combining two commands into "Perform power off after detecting the module". After optimization, the system re-summarizes the instruction set and calls the template engine again to output the final integrated text. If there are still logical contradictions, then start the next round of conflict detection and adjustment until a final operation and maintenance guidance text that is consistent, clear, and conflict-free is generated.

[0061] For example, when an alarm of "High-voltage module abnormality" appears at an electric vehicle charging station, the system generates the following operation steps through structured knowledge: "Disconnect the main power supply", "Check the high-voltage connection line", and "Measure the voltage of the high-voltage module". The initially generated operation and maintenance instructions are: Please immediately disconnect the main power supply; Please detect the module voltage; Please check the high-voltage connection line. The system identifies a logical conflict between the operations of "power off" and "voltage detection" during conflict detection because voltage measurement cannot be performed after power off. The system adjusts the execution order according to the rules and optimizes it to: Please detect the module voltage; Please check the high-voltage connection line; Please disconnect the main power supply after the detection is completed. The final output is an operation and maintenance guidance text with clear structure and no conflicting instructions, ensuring operation coherence and execution safety.

[0062] The adaptation module integrates a complex scenario adaptation module, triggers external data adjustment instructions adapted to the fault codes and environmental parameters parsed according to the device status, and obtains the adapted guidance solution, including obtaining device status data in complex scenarios, parsing the signal characteristics of the triggered fault codes, determining the initial fault status, extracting key variables from the environmental parameters through the parsing process, obtaining environmental impact factors associated with the fault codes. If the environmental impact factor exceeds the preset threshold, the instruction content is adjusted according to the external data adjustment instructions to generate temporary optimization instructions. For the temporary optimization instructions, an adaptation adjustment mechanism is adopted to integrate the fault codes and environmental parameters to obtain a fine-tuning instruction set, record the revision log of the fine-tuning instruction set, store the adjustment details, generate a dynamic version of the optimization plan, obtain real-time feedback on the device status according to the dynamic version of the optimization plan, judge the execution effect of the instructions. If the execution effect does not meet the preset standard, the fault codes are re-parsed through the trigger mechanism to obtain an updated optimization plan.

[0063] This module first receives the device status data of the charging pile, which includes voltage, current, temperature, humidity, module load rate, etc. The system uses a signal detection algorithm to identify abnormal fluctuation characteristics in the data, such as instantaneous surges and drops in current, abnormal temperature gradient changes, etc., and determines the initial fault state by comparing the feature pattern matching with the built-in fault code analysis rules. Subsequently, key variables related to the current fault type, such as ambient temperature, humidity, wind speed, etc., are extracted from the environmental parameter set, and each parameter is evaluated through the impact factor calculation function. The impact factor scoring function is defined as follows: Environmental impact score = current value minus reference benchmark value, divided by the benchmark tolerance range. If the score result is greater than 1, it means that the current environmental variable exceeds the normal tolerance range, that is, it is judged as a "key environmental impact factor". When the environmental impact factor is abnormal, the system adjusts the initial operation suggestion in combination with external data sources (such as weather API, device historical response data). For example, in a high temperature scenario, the "restart module immediately" instruction is adjusted to "restart module after cooling for 10 minutes" to generate a "temporary optimization instruction". The instruction will enter the "adaptation adjustment mechanism", which generates a fine-tuning instruction set by integrating the following two dimensions of information: the semantic vector of the fault code, which matches the processing method in the historical fault instance; the normalized vector of the environmental variable, which participates in the decision weight adjustment as a constraint factor. The decision logic of the final fine-tuning instruction is expressed as: instruction strength = fault code historical resolution rate × weight coefficient + environmental adaptability × correction factor. Among them, the weight coefficient and correction factor are system experience parameters (such as 0.6 and 0.4), which are used to balance the adaptation contribution of historical experience and the current environment. After the system generates the fine-tuning instruction set, it records the revision source, environmental influencing factors, adjustment reasons and other information of each instruction, writes them into the revision log, and forms a "dynamic version" of the optimization plan. Subsequently, the system enters the instruction effect evaluation process: real-time collection of device status feedback after the execution of the instruction, such as whether the power is restored, whether the voltage is stable, whether the alarm is lifted, etc. If the set indicators (such as "voltage stabilization time is less than 5 seconds" and "module temperature drops by more than 3°C") are not achieved, the system automatically triggers a new round of fault code analysis mechanism, and generates a new optimization plan in combination with the latest environmental parameters to form an adaptive adjustment closed loop.

[0064] For example, taking the charging piles in a highway service area as an example, there was a sudden drop in module current during the noon period, accompanied by a temperature control alarm. The system identified the fault code as "module over-temperature automatic protection", and collected the ambient temperature as high as 39°C and the humidity as 78%. The impact factor score shows that the ambient temperature exceeds the standard threshold by more than 20%. The system adjusts the initial instruction "restart the module immediately" to "wait for the temperature to drop before starting the fan and delay starting the module for 5 minutes", and writes the adjustment to the revision log. After execution, real-time feedback data shows that the module temperature dropped by 4.2°C and the current returned to the stable range. The system records the version number, adjustment source and effect evaluation of this round of optimization plan for subsequent reference.

[0065] The feedback processing module obtains the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update. This includes obtaining the feedback data from the interaction records, judging the data integrity through a preset threshold to obtain a filtered feedback data set, using the experience replay mechanism to store the filtered feedback data set and action records, generating a structured replay buffer, analyzing the action records in the replay buffer through an exploration strategy, adjusting the action selection probability to obtain an optimized action distribution, updating the model parameters according to the optimized action distribution, iteratively training using the gradient descent algorithm to generate an updated classification network. If the confidence level of the problem type output by the classification network is lower than the preset threshold, obtain similar problem records from the historical solutions, judge the matching parameters, combine the matching parameters with the output of the classification network to adjust the weights of the historical solutions, obtain an accurate problem type classification result, generate corresponding solution parameters according to the accurate problem type classification result, and store them in the replay buffer to optimize subsequent interactions.

[0066] After the user finishes executing the guidance instructions, the feedback processing module collects the corresponding execution feedback information and operation records. The information includes: operation response time, whether the fault is resolved, changes in device status parameters, etc. First, the system filters the feedback data according to the set integrity threshold (such as at least three types of keyword fields need to be included), and retains the valid data to form a "filtered feedback data set". This data set corresponds one-to-one with the operation steps executed by the operation and maintenance personnel and is jointly stored in the "experience replay buffer". The form of each record is: state s, action a, feedback r, next state s'. Subsequently, the system performs action analysis through the ε-greedy strategy, that is: the system explores new operation actions with a certain probability (ε value, for example, 0.1), and selects the current optimal action with the remaining probability. The action selection probability Pi(t) is adjusted according to the feedback reward function, and its update method is: P i (t + 1)=P i (t)+α×[r - P i (t)]. Where: P i(t) is the selection probability of action i at time t; r is the feedback score corresponding to the action in the current replay record; α is the learning rate (such as 0.05), representing the speed of action probability adjustment. The action distribution calculated by this strategy is used to optimize the policy network or the policy layer of the classification model. The system takes the new optimized action distribution as the target and uses the gradient descent algorithm to adjust the parameters of the current classification network. The loss function is the cross-entropy error between the predicted classification output and the true feedback, and the model weights are updated through the backpropagation of the error in each round. If after the latest round of training, the output confidence of the network for a certain type of problem is lower than the threshold (such as 0.5), it indicates that the model has not fully learned the characteristics of this type of problem. At this time, the system obtains case records similar to the current problem feature vector from the historical solution database, and comprehensively scores by comparing matching parameters (such as feature vector similarity, environmental variable matching degree, etc.). The system adjusts the weights of the historical solutions according to the following method: Solution weight = Classification network confidence × β + Matching parameter score × (1 - β). Among them: β is the confidence ratio adjustment parameter (such as 0.6), used to balance the model output and historical experience; the matching parameter score comes from the weighted average of semantic similarity and scenario matching degree. The finally output new classification result is considered to be more accurate, and the system records the corresponding solution parameters and rewrites them into the experience replay buffer for the next round of interaction optimization.

[0067] For example, at a charging station in a high-humidity area in the south, after the operation and maintenance personnel performed the operation "restart the voltage module" recommended by the system, the feedback was "restart failed, the module has no response". The system recorded the "operation failed" status and the current environmental parameters (humidity 92%, temperature 36°C), and determined that the feedback was valid. This feedback was written into the experience replay buffer. By reviewing other operation records in similar scenarios in the replay, the system found that the success rate of the "power off first and then warm start" solution in similar scenarios was 78%, while the confidence of the current model for this solution was only 0.46. Therefore, the system introduced the historical record HX115 with a matching similarity of 0.83, and after adjustment, re-output the recommended solution: "Disconnect the main power supply and then restart the module after a 5-second delay". The system took this solution as the new recommended operation and recorded its adjustment process for subsequent learning.

[0068] Feedback processing module, which obtains the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update. This includes analyzing the input data through a problem classification network to obtain the classification weight and matching threshold. If the classification weight is lower than the preset threshold, the network parameters are adjusted to obtain the optimized classification weight. Features are extracted from the historical solutions, combined with the optimized classification weight to determine the matching threshold. If the deviation between the matching threshold and the features of the historical solutions exceeds the range, the threshold is adjusted by weighted average to obtain a stable matching threshold. A convergence evaluation method is used to process the optimized classification weight and the stable matching threshold to judge the model stability. If the convergence evaluation result shows fluctuations, the parameters are adjusted through iterative optimization to obtain stable model parameters. The feedback data is obtained, its relevance to the dynamic rule base is analyzed, and the content of the rule base is updated. The feedback data is fused through a data integration method to generate an updated dynamic rule base. According to the updated dynamic rule base, the scenario label features are extracted to generate preliminary mapping rules. If the matching degree between the preliminary mapping rules and the scenario labels is insufficient, the rules are adjusted and optimized to obtain optimized mapping rules. The scenario labels are processed through the optimized mapping rules to generate the final scenario label mapping result. The mapping result is verified by consistency check to obtain the final scenario label mapping rules. The key parameters are extracted from the final scenario label mapping rules to update the initial configuration of the problem classification network, and the network structure is adjusted through parameter feedback to obtain an optimized classification network model.

[0069] After receiving the operation and maintenance problem text or feature vector, the system first performs forward propagation through the classification network, outputs the probability distribution of each problem type, and extracts the highest weight value as the classification weight. If this weight value is lower than the set confidence threshold (such as 0.65), it is considered that the current network has insufficient confidence in classifying this problem. The system calls the network adjustment function, calculates the gradient based on the current classification error, and updates the neural network parameters (such as the weight matrix and bias term) to obtain a more reliable optimized classification weight. This process uses the standard backpropagation and gradient descent algorithm, and the update rule is: weight update = current weight - learning rate × current gradient. At the same time, the system extracts the feature distribution associated with the current problem label from the historical solution library, compares the Euclidean distance or cosine similarity between the current input feature vector and the historical feature vector, calculates its matching degree, and derives the current matching threshold. If the deviation between this matching threshold and the historical average value is greater than the set range (such as 10%), the system makes an adjustment through weighted averaging: stable matching threshold = current threshold × α + historical mean × (1 - α). Here, α is the balance coefficient, and the recommended value is 0.4. To ensure the stability of the model parameters, the system calls the convergence evaluation function after each round of update. This function statistics the change rate of the classification weight in the recent N times (such as N = 5). If the fluctuation is greater than the set standard deviation (such as σ > 0.1), it triggers another optimization iteration. This continues until the model output fluctuation converges. In the feedback processing link, the system structurally compares the collected execution feedback and interaction data to analyze whether it matches the rule pattern in the current dynamic rule library. If not, the system will execute the rule update algorithm, merge the historical rules based on the new data, and generate an updated dynamic rule library. According to the rule library, the system constructs a preliminary mapping relationship between "scenario label - operation suggestion", and compares the semantic features of the known labels to calculate the semantic matching degree (such as based on vector cosine similarity). If the matching degree is insufficient (such as less than 0.7), the system will adjust the rule content or add auxiliary conditions to generate a more accurate optimized mapping rule. Finally, the optimized mapping rule is used to regenerate the scenario label result, and the system performs a consistency check on it (such as rule application closed-loop detection) to ensure that there are no contradictions in terms of semantics, structure, and logic. This final mapping structure will extract key parameters, such as labels, context conditions, priority factors, etc., and send them back to the classification network to update the model structure (such as adjusting the number of hidden layer nodes, activation function configuration, etc.), thereby realizing the dynamic evolution of the classification model structure.

[0070] For example, an operation platform receives an exception report: "The charging pile No. 9 at the site fails to start and the screen has no display". The system classification network outputs a weight of "start-up exception" as 0.58, which is lower than the threshold of 0.65, triggering model optimization. The historical solutions show that such problems are often caused by "damage to the power control module", and the feature similarity is 0.73, which is lower than the average of 0.81. The system adjusts the matching threshold to 0.76, and takes "return to normal after replacing the power module" as a new input through feedback to correct the matching rule. Subsequently, a new label "power control failure" is remapped and generated, and the optimization result is used to update the classification network structure. The recognition accuracy of subsequent similar events is increased to 0.84, and the stability of the classification output is significantly enhanced.

[0071] As Figure 2 shown, a method for intelligent digital operation and maintenance management of a charging pile is also provided. Using the described intelligent digital operation and maintenance management system for a charging pile, the method includes determining a delay information flow channel from the charging pile operation and maintenance management system, obtaining real-time discussion content, processing the discussion text using word segmentation technology to obtain semantic feature vectors; if the confidence level of the semantic feature vectors is higher than a preset threshold, determining it as a specific problem type through state space definition to obtain a classified problem label; obtaining associated features with the historical problem library, matching historical problem records, evaluating the matching accuracy through reward function design, and determining the historical solution most similar to the current problem; extracting key operation steps, mapping and associating the operation steps with scenario labels, sorting the execution order of emergency scenarios to obtain a structured knowledge representation; generating an actionable guide, converting the operation steps into executable instructions for operation and maintenance personnel using templated text generation technology, detecting and identifying logical contradictions between the instructions to determine the final guide text; integrating a complex scenario adaptation module, triggering an external data adjustment instruction content adapted to the fault codes parsed according to the device status and environmental parameters to obtain an adapted guide solution; obtaining feedback data after execution, storing feedback and interaction records, adjusting and optimizing action selection and model parameter updates; obtaining optimized classification weights and matching thresholds, evaluating the model stability using a convergence evaluation model, and updating the dynamic rule library through feedback data integration to obtain an updated scenario label mapping rule.

[0072] This method first obtains real-time discussion texts from the charging pile operation and maintenance management system, converts them into semantic feature vectors through word segmentation and semantic embedding models, and determines whether their confidence levels are higher than the set thresholds. If the conditions are met, the problem type is determined by combining the state space model, and problem tags are generated. Subsequently, based on the tags and semantic vectors, the system searches for similar cases in the historical problem library, calculates the semantic similarity and tag consistency scores, evaluates the matching accuracy through a reward function, and selects the most similar historical solution. The operation steps in the solution are extracted, de-duplicated semantically, and then matched with the scenario tags in the knowledge graph. Combining with the scenario priorities, a structured operation sequence is generated. Through the templated text generation module, the system converts the operation steps into natural language instructions, performs logical consistency detection, and conducts semantic reconstruction or sequence adjustment when contradictions are found to form a conflict-free final guidance text. At the same time, the adaptation module analyzes whether the current device status and environmental variables, such as temperature and humidity, affect the operation. If so, the instruction content is dynamically adjusted to generate a "fine-tuned instruction set" that integrates device features and environmental factors. After the operation and maintenance personnel execute the instructions, the system collects feedback information and records data such as the operation success rate and response time, and updates the action selection probability and classification model parameters through reinforcement learning methods. If the confidence level of the classification result is insufficient or the output fluctuates greatly, the system will introduce historical solutions for parameter weighted correction and iteratively optimize the model until convergence. Finally, the system adjusts the scenario tag mapping rules according to the feedback data, obtains the final rule structure through consistency verification, and updates the initial configuration of the problem classification model to achieve the continuous learning and dynamic optimization of the operation and maintenance system.

[0073] For example, in an operation center in an inland city, a discussion message: "Pile No. 8 trips frequently and cannot be restarted successfully" is captured by the system and converted into a semantic vector. Matching the historical case "Restarting without cooling after module overload", the system's recommended solution is "Cut off the power and cool for 10 minutes before restarting". The templated instruction is: "Please cut off the power and wait for 10 minutes, then restart the power module". Since the current outdoor temperature is higher than 35°C, the system adaptation module automatically adjusts the cooling time to 15 minutes. The feedback after execution shows that the tripping is resolved, and the system updates the rule library based on this record and adjusts the mapping strategy for "Module overload in high-temperature scenarios" to enhance the response efficiency for the next similar scenario.

[0074] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent digital operation and maintenance management system for charging piles, characterized in that, Including: An information acquisition module, which determines a delayed information transfer channel from a charging pile operation and maintenance management system, acquires real-time discussion content, processes the discussion text using word segmentation technology, and obtains a semantic feature vector; A problem identification module, if the confidence level of the semantic feature vector is higher than a preset threshold, determines it as a specific problem type through state space definition, and obtains a classified problem label; A historical matching module, which acquires associated features with a historical problem library, matches historical problem records, designs a reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current problem; A step extraction module, which extracts key operation steps, maps and associates the operation steps with scenario labels, sorts the execution order of emergency scenarios, and obtains a structured knowledge representation; A guidance generation module, which generates actionable guidance, uses templated text generation technology to convert the operation steps into executable instructions for operation and maintenance personnel, detects and identifies logical contradictions between the instructions, and determines the final guidance text; An adaptation module, which integrates a complex scenario adaptation module, triggers an external data adjustment instruction content adapted to the fault code and environmental parameters parsed according to the device state, and obtains an adapted guidance solution; A feedback processing module, which acquires the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update; A rule update module, which acquires optimized classification weights and matching thresholds, uses a convergence evaluation to model stability, and updates the dynamic rule library through feedback data integration to obtain an updated scenario label mapping rule; The feedback processing module, which acquires the feedback data after execution, stores the feedback and interaction records, and adjusts and optimizes the action selection and model parameter update includes: Acquiring feedback data from the interaction records, judging the data integrity through a preset threshold, obtaining a filtered feedback data set, storing the filtered feedback data set and action records using an experience replay mechanism, generating a structured replay buffer, analyzing the action records in the replay buffer through an exploration strategy, adjusting the action selection probability, obtaining an optimized action distribution, updating the model parameters according to the optimized action distribution, iteratively training using a gradient descent algorithm, generating an updated classification network, if the confidence level of the problem type output by the classification network is lower than the preset threshold, obtaining similar problem records from the historical solutions, judging the matching parameters, combining the matching parameters with the output of the classification network, adjusting the weights of the historical solutions, obtaining an accurate problem type classification result, generating corresponding solution parameters according to the accurate problem type classification result, and storing them in the replay buffer to optimize subsequent interactions; The rule update module, which acquires optimized classification weights and matching thresholds, uses a convergence evaluation to model stability, and updates the dynamic rule library through feedback data integration to obtain an updated scenario label mapping rule includes: Analyze the input data through the problem classification network to obtain the classification weight and the matching threshold. If the classification weight is lower than the preset threshold, adjust the network parameters to obtain the optimized classification weight. Extract features from the historical solutions, and combine the optimized classification weight to determine the matching threshold. If the deviation between the matching threshold and the features of the historical solutions exceeds the range, adjust the threshold through weighted average to obtain a stable matching threshold. Use the convergence evaluation method to process the optimized classification weight and the stable matching threshold to judge the model stability. If the convergence evaluation result shows fluctuations, adjust the parameters through iterative optimization to obtain stable model parameters. Obtain the feedback data, analyze its relevance to the dynamic rule base, update the content of the rule base, fuse the feedback data through the data integration method to generate an updated dynamic rule base. According to the updated dynamic rule base, extract the scenario label features to generate preliminary mapping rules. If the matching degree between the preliminary mapping rules and the scenario labels is insufficient, optimize through rule adjustment to obtain optimized mapping rules. Process the scenario labels through the optimized mapping rules to generate the final scenario label mapping result. Use consistency verification to verify the mapping result to obtain the final scenario label mapping rules. Extract the key parameters from the final scenario label mapping rules to update the initial configuration of the problem classification network, and adjust the network structure through parameter backpropagation to obtain an optimized classification network model.

2. The intelligent digital operation and maintenance management system for a charging pile according to claim 1, wherein: The information acquisition module determines the delay information transfer channel from the charging pile operation and maintenance management system, obtains the real-time discussion content, and processes the discussion text using the word segmentation technology to obtain semantic feature vectors, including: Extract cross-regional communication data from the charging pile operation and maintenance log, use distributed message queue technology to process high-concurrency requests, generate a structured instant messaging data set. If the data volume exceeds the preset threshold, distribute the data to multiple queue nodes through a partitioning strategy to obtain a structured instant messaging data set. For the structured instant messaging data set, establish a real-time collaboration platform using the WebSocket protocol to achieve cross-regional multi-role two-way communication, determine a low-latency information transfer channel, monitor the channel status through the real-time collaboration platform. If it is detected that the latency exceeds the preset threshold, adjust the load balancing of the communication nodes to obtain a stable low-latency channel. Obtain real-time discussion text from the low-latency information transfer channel, process the text using the word segmentation technology to generate a word segmentation sequence of the text, use a pre-trained word embedding model to convert the word segmentation sequence into a word vector sequence to obtain a preliminary semantic representation of the text. Obtain temperature and humidity environmental parameter data through the sensor interface, normalize the environmental parameters using standardization processing to generate an environmental feature vector. If the dimension of the environmental feature vector does not match the dimension of the semantic representation, adjust the dimension through linear transformation to obtain an aligned environmental feature vector. For the preliminary semantic representation of the text and the aligned environmental feature vector, use the attention mechanism to fuse the information of both to generate a comprehensive semantic feature vector, process the comprehensive semantic feature vector through a multi-layer perceptron to obtain the final semantic feature vector of the problem description. Extract key semantic units from the final semantic feature vector, use a clustering algorithm to group the semantic units to generate a semantic classification result of the problem description, and generate an optimized scheduling instruction for cross-regional collaboration according to the semantic classification result to determine the priority ranking of the operation and maintenance tasks.

3. The intelligent digital operation and maintenance management system for a charging pile according to claim 1, wherein: The problem identification module, if the confidence level of the semantic feature vector is higher than the preset threshold, is judged as a specific problem type through state space definition, and the classified problem labels obtained include: Extract the semantic feature vector from the problem description, perform feature encoding using a pre-trained deep learning network to obtain an initial semantic representation, perform classification processing on the initial semantic representation through the pre-trained deep learning network to obtain a classification confidence distribution. If the classification confidence distribution is higher than the preset threshold, use a state space model to verify the problem type to determine a preliminary problem type label. According to the preliminary problem type label, obtain an association rule from the preset knowledge base to get a label correction basis, adjust the preliminary problem type label through the label correction basis to generate a corrected problem type label, use the corrected problem type label, combined with the semantic feature vector, to construct a label semantic consistency check to judge the final problem type label, extract the classification result from the final problem type label to generate a problem type classification output.

4. An intelligent digital operation and maintenance management system for a charging pile according to claim 1, characterized in that: The historical matching module, obtains the associated features with the historical problem library, matches the historical problem records, designs a reward function to evaluate the matching accuracy, and determines the historical solution most similar to the current problem, including: Obtain classification tags from the input problem tags, generate associated features through feature extraction methods to obtain a feature vector representation. Through a vector similarity calculation method, compare the feature vector with the feature vectors recorded in the historical problem database to obtain a similarity score. If the similarity score is greater than a preset threshold, obtain the corresponding matching record from the historical problem database to determine the candidate historical solution. Adopt a reward function, combine the classification tags and the context information of the matching record, calculate the evaluation score of each candidate solution to obtain a precision ranking. According to the precision ranking, obtain the record with the highest score from the candidate solutions to determine the most similar historical solution. By analyzing the differences between the feature vector of the most similar solution and the current problem tags, adjust the feature weights to obtain an optimized solution representation. Extract key parameters from the optimized solution representation to generate the final solution output.

5. The intelligent digital operation and maintenance management system for a charging pile according to claim 1, wherein: The above-mentioned step extraction module extracts key operation steps, maps and associates the operation steps with scenario tags, and sorts the execution order of emergency scenarios to obtain a structured knowledge representation including: Obtain records from the historical solution database, adopt text parsing technology to extract key operation steps to obtain an operation step set. If the operation step set contains duplicate items, generate a unique operation step list through deduplication processing to obtain a refined step set. Adopt knowledge graph technology to perform semantic matching on the refined step set and preset scenario tags to generate a mapping relationship between steps and tags. According to the mapping relationship, obtain the scenario tag weights corresponding to each operation step, judge whether the weights exceed the preset threshold to obtain a set of highly relevant tags. For the set of highly relevant tags, adopt a priority sorting algorithm to sort the emergency scenarios according to the instruction priority to generate a scenario priority sequence. Through structured encoding technology, associate the scenario priority sequence with the operation step set to generate a structured knowledge representation. If there are missing fields in the structured knowledge representation, fill in the missing parts through semantic completion technology to obtain a complete knowledge representation.

6. The intelligent digital operation and maintenance management system for a charging pile according to claim 1, characterized in that: The above-mentioned guidance generation module generates actionable guidance, adopts templated text generation technology to convert operation steps into executable instructions for operation and maintenance personnel, and detects and identifies logical contradictions between instructions to determine the final guidance text including: Obtain structured knowledge, extract operation and maintenance-related rules and data from a preset knowledge base to obtain a knowledge representation. Adopt templating technology to generate initial operation and maintenance instructions for the rules and data in the knowledge representation to determine an executable instruction set. Through conflict detection, analyze the logical relationships in the executable instruction set. If there are contradictions, mark the relevant instructions to obtain a contradiction identifier. According to the contradiction identifier, perform rule analysis, extract the conflict points from the marked instructions to determine a logical contradiction set. For the logical contradiction set, adopt text transformation technology to adjust the expression or order of the contradictory instructions to obtain an optimized instruction set. Through templating technology, integrate the optimized instruction set to generate the final text to determine the operation and maintenance guidance text. If there are still contradictions in the optimized instruction set, repeat conflict detection and rule analysis to obtain the final contradiction-free guidance text.

7. The intelligent digital operation and maintenance management system for charging piles according to claim 1, wherein: The adaptation module integrates a complex scenario adaptation module, triggers an external data adjustment instruction content adapted from the fault code parsed according to the device state and environmental parameters, and the obtained adapted guidance solution includes: Obtain the device state data in complex scenarios, analyze the signal characteristics that trigger the fault code, determine the initial fault state. Through the analysis process, extract the key variables from the environmental parameters to obtain the environmental impact factors associated with the fault code. If the environmental impact factor exceeds the preset threshold, generate a temporary optimization instruction according to the external data adjustment instruction content. For the temporary optimization instruction, adopt an adaptation adjustment mechanism to integrate the fault code and environmental parameters to obtain a refined instruction set. Record the revision log of the refined instruction set, store the adjustment details, generate a dynamic version of the optimization plan. According to the dynamic version of the optimization plan, obtain the real-time feedback of the device state, judge the execution effect of the instruction. If the execution effect does not meet the preset standard, re-analyze the fault code through the trigger mechanism to obtain an updated optimization plan.

8. A method for intelligent digital operation and maintenance management of a charging pile, which adopts the intelligent digital operation and maintenance management system of a charging pile described in any one of claims 1-7, characterized in that, The method includes: Determine the delay information transfer channel from the charging pile operation and maintenance management system, obtain the real-time discussion content, and process the discussion text using word segmentation technology to obtain the semantic feature vector; If the confidence of the semantic feature vector is higher than the preset threshold, judge it as a specific problem type through state space definition to obtain the classified problem label; Obtain the associated features with the historical problem library, match the historical problem records, design a reward function to evaluate the matching accuracy, and determine the historical solution most similar to the current problem; Extract the key operation steps, map and associate the operation steps with the scenario labels, sort the execution order of emergency scenarios, and obtain a structured knowledge representation; Generate actionable guidance, use template-based text generation technology to convert the operation steps into executable instructions for operation and maintenance personnel, detect and identify logical contradictions between instructions, and determine the final guidance text; Integrate a complex scenario adaptation module, trigger an external data adjustment instruction content adapted from the fault code parsed according to the device state and environmental parameters, and obtain an adapted guidance solution; Obtain the feedback data after execution, store the feedback and interaction records, and adjust and optimize the action selection and model parameter update; Obtain the optimized classification weights and matching thresholds, use a convergence evaluation model to evaluate the stability, and update the dynamic rule library through feedback data integration to obtain an updated scenario label mapping rule.

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